PerfMamba: Performance Analysis and Pruning of Selective State Space Models

Modern sequence models are becoming increasingly powerful, but their computational cost, memory behavior, and deployment efficiency remain important challenges. This is especially relevant for emerging architectures such as Mamba and selective state space models, which have been proposed as efficient alternatives to Transformer-based sequence models.

Our paper, “PerfMamba: Performance Analysis and Pruning of Selective State Space Models,” presents an empirical study of Mamba-1 and Mamba-2. The work analyzes runtime behavior, resource utilization, computation patterns, memory access, I/O characteristics, and scaling properties across sequence lengths from 64 to 16,384 tokens.

The study shows how different components of selective state space models contribute to performance and efficiency. In particular, the paper examines the SSM component, which plays a central role in Mamba-style architectures, and identifies opportunities for optimization through pruning.

Based on the profiling results, PerfMamba explores pruning low-activity states in the SSM component to improve throughput and reduce memory usage while maintaining model accuracy under moderate pruning.

This work contributes to research on Mamba, selective state space models, performance analysis, runtime profiling, model pruning, model compression, efficient AI, high-performance computing, and hardware-aware optimization.

Researchers working on large language models, sequence modeling, AI systems, neural network acceleration, scalable machine learning, and efficient deep learning may find this work useful.

Paper

PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu, Juan Pablo Muñoz, Ali Jannesari

Published in: International Symposium on Benchmarking, Measuring and Optimization
Publisher: Springer, Singapore
Year: 2026
Pages: 27–44

Paper page: https://mobinakashaniyan.github.io/publications/perfmamba/

PDF: https://mobinakashaniyan.github.io/papers/perfmamba-performance-analysis-pruning-selective-state-space-models.pdf

Keywords

PerfMamba, Mamba, Mamba-1, Mamba-2, selective state space models, state space models, SSM, sequence modeling, Transformer alternatives, performance analysis, runtime profiling, benchmarking, resource utilization, memory access patterns, I/O characteristics, scaling analysis, model pruning, state pruning, model compression, efficient AI, high-performance computing, hardware-aware optimization, large language models, AI systems, neural network acceleration.

Citation

Al Asif, A., Kashaniyan, M., Yu, S., Muñoz, J. P., & Jannesari, A. (2026). PerfMamba: Performance analysis and pruning of selective state space models. In International Symposium on Benchmarking, Measuring and Optimization (pp. 27–44). Springer, Singapore.